Current environmental monitoring systems assume particles to be spherical, and do not attempt to classify them. A laser-based sys(cid:173) tem developed at the University of Hertfordshire aims at classify(cid:173) ing airborne particles through the generation of two-dimensional scattering profiles. The pedormances of template matching, and two types of neural network (HyperNet and semi-linear units) are compared for image classification. The neural network approach is shown to be capable of comparable recognition pedormance, while offering a number of advantages over template matching.

An instrument to detect particle shape and size from spatial light scattering profiles has High-speed Airborne Particle Monitoring Using Artificial Neural Networks 981 previously been described [6]. The system constrains individual particles to traverse a laser beam. Thus, spatial distributions of the light scattered by individual particles may be recorded as two dimensional grey-scale images. Due to their highly distributed nature, Artificial Neural Networks (ANNs) offer the possibility of high-speed nonlinear pattern classification. Their use in particulate classification has already been investigated. The work by Kohlus [7] used contour data extracted from microscopic images of particles, and so was not real-time. While using laser scattering data to allow real-time analysis, Bevan [2] used only three photomultipliers, from which very little shape information can be collected. This paper demonstrates the plausibility of particle classification based on shape recognition using an ANN. While capable of similar recognition rates, the neural networks are shown to offer a number of advantages over template matching.

An instrument to detect particle shape and size from spatial light scattering profiles has High-speed Airborne Particle Monitoring Using Artificial Neural Networks 981 previously been described [6]. The system constrains individual particles to traverse a laser beam. Thus, spatial distributions of the light scattered by individual particles may be recorded as two dimensional grey-scale images. Due to their highly distributed nature, Artificial Neural Networks (ANNs) offer the possibility of high-speed nonlinear pattern classification. Their use in particulate classification has already been investigated. The work by Kohlus [7] used contour data extracted from microscopic images of particles, and so was not real-time. While using laser scattering data to allow real-time analysis, Bevan [2] used only three photomultipliers, from which very little shape information can be collected. This paper demonstrates the plausibility of particle classification based on shape recognition using an ANN. While capable of similar recognition rates, the neural networks are shown to offer a number of advantages over template matching.

An instrument to detect particle shape and size from spatial light scattering profiles has High-speed Airborne Particle Monitoring Using Artificial Neural Networks 981 previously been described [6]. The system constrains individual particles to traverse a laser beam. Thus, spatial distributions of the light scattered by individual particles may be recorded as two dimensional grey-scale images. Due to their highly distributed nature, Artificial Neural Networks (ANNs) offer the possibility of high-speed nonlinear pattern classification. Their use in particulate classification has already been investigated. The work by Kohlus [7] used contour data extracted from microscopic images of particles, and so was not real-time. While using laser scattering data to allow real-time analysis, Bevan [2] used only three photomultipliers, from which very little shape information can be collected. This paper demonstrates the plausibility of particle classification based on shape recognition using an ANN. While capable of similar recognition rates, the neural networks are shown to offer a number of advantages over template matching.